Mobile Tourist Information System With Safety Recommender Component

Vyklyuk, Yaroslav; System, Computer; Sydor, P. Ya.; Savchuk, Valeriia

doi:10.12709/mest.06.06.02.19

Cited by 2 publications

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“…Multivariate statistical analysis (such as factor analysis (FA) and partial least squares (PLS)) was used to process data questionnaires collected from thirty respondents [5,6]. rough the research, he found that when designing the user interface of a mobile parent information system, two important emotional factors must be considered, namely, interest and benefit [7,8]. But these two factors cannot determine everything.…”

Section: Related Contentmentioning

confidence: 99%

College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems

Zhang

Chen

2021

Mobile Information Systems

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With the development of mobile information, artificial intelligence technology has developed rapidly, which is changing everything in our life. Meanwhile, smart education appeared and attracted much attention; however, there is still a lack of systematic discussion on how to take the road of artificial intelligence plus smart classroom. In order to promote the intelligent development of education, this article mainly talks about the application of artificial intelligence technology to college English smart classrooms. In the process of teaching, a new teaching model of college English smart classroom was designed, and the teaching model was carried out through the experimental class and the survey results of the students. The survey results show that students’ satisfaction with this teaching model is as high as 80%.

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Section: Related Contentmentioning

confidence: 99%

College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems

Zhang

Chen

2021

Mobile Information Systems

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“…When there are many sensors sending data to the most advanced equipment [10], whether it is physical data or network data, it must be cleared by the data processing unit inside the device [11,12]. After that, the data are uploaded to the cloud in a predefined format [13,14]. According to the system operation target and application script design, the first is the edge device [15].…”

Section: The Methods Of Continuous Use Behavior Of Knowledge Payment Platform Based On Edge Computing Under Mobile Information Systemmentioning

confidence: 99%

Continuous Use Behavior of Knowledge Payment Platform Based on Edge Computing under Mobile Information System

Huo

2021

Mobile Information Systems

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From 2015 to the end of 2016, the Internet set off a wave of payment for knowledge. Pay-for-knowledge platforms such as Fenda, iGet, and Qianliao went online quickly, and platforms such as Himalaya FM, Zhihu, and Qingting FM gathered to launch paid columns. The number of users increased rapidly, and payment for knowledge was considered to have reached the trend of development. This article aims to study the introduction of edge computing in the mobile information system into the existence and inevitability of the knowledge payment platform; analyze the advantages, dilemmas, and optimization paths of the knowledge payment platform; and try to provide a theoretical reference for promoting its development. This article puts forward an explanation of the related content of mobile edge computing and RFID technology overview, using comparative experiment and behavior analysis methods. The experimental results show that there are 98 people under the age of 18 in the questionnaire, accounting for 19.1% of the total, 201 people aged 18–29, accounting for 39.1% of the total, 142 people aged 30–39, accounting for 27.6% of the total, and 73 people over 40 years old, accounting for 14.2% of the total number. It can be seen from the data that the sample age is mainly concentrated in the 18–29 years old, followed by the 30–39 years old; the sample age is biased towards young people. It has well completed the continuous use behavior analysis of the knowledge payment platform based on edge computing under the mobile information system.

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Mobile Tourist Information System With Safety Recommender Component

Cited by 2 publications

References 0 publications

College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems

College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems

Continuous Use Behavior of Knowledge Payment Platform Based on Edge Computing under Mobile Information System

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